CLIMATE LLC

A computer system and computer-implemented techniques for determining and presenting improved seeding rate recommendations for planting seeds in a field are provided. In an embodiment, a computer-implemented method includes receiving digital data representing planting parameters including seed type information and planting row width, and retrieving a set of seeding models based upon the planting parameters, where each of the seeding models includes a regression model defining a relationship between plant yield and seeding rate on a specific field. The method also includes generating an empirical mixture model as a composite distribution of the set of seeding models, generating a seeding rate distribution for the planting parameters based upon the empirical mixture model, and calculating a seeding rate recommendation based on the seed rate distribution. The method then also includes planting plant seeds in the specific field consistent with the seeding rate recommendation.

An example computer-implemented method includes receiving a plurality of agricultural data records including yield properties of products grown in fields and raw field features of the fields. The method also includes transforming the raw field features into distinct feature classes that characterize key features affecting yield of the one or more products, and generating, using data from the plurality of agricultural data records and the distinct feature classes, genomic-by-environmental relationships between one or more products, yield properties of the one or more products, and field features associated with the one or more products. Further, the method includes generating, based at least in part on the genomic-by-environmental relationships, predicted yield performance for a set of products associated with one or more target environments, generating product recommendations for the one or more target environments based on the predicted yield performance for the set of products, and providing one or more instructions configured to cause display of the product recommendations.

Techniques are provided for generating target success group of hybrid seeds for target fields include a server receiving agricultural data records that represent crop seed data describing seed and yield properties of hybrid seeds and first field geo-location data for agricultural fields where the hybrid seeds were planted. The server receives second geo-locations data for target fields where hybrid seeds are to be planted. The server generates a dataset of hybrid seed properties that include yield values and environmental classifications for hybrid seeds and then a dataset of success probability scores that describe the probability of a successful yield on the target fields based on the dataset of hybrid seed properties and the second geo-location data. The server generates target success yield group of hybrid seeds and probability of success values based on success probability scores and a yield threshold. The server causes display of the target success yield group.

Techniques for recommending side-by-side plantings of pairs of hybrids or seeds include a server computer receiving agricultural data records that represent crop seed data describing seed and yield properties of hybrid seeds and first data for agricultural fields where the hybrid seeds were planted. The server receives second data for available hybrids and seeds and automatically calculates a dataset of success probability scores that describe the probability of a successful yield on the target fields. Data is organized as pairs to facilitate comparison of actual plantings to optimized plantings that have a probability of success (POS), in terms of yield lift or increased yield season-over-season, for different yield values. Confidence values are generated and stored in association with the POS values and can be used as a basis of visual output to support planting and/or field management decisions as part of an automated intelligent agricultural decision support system.

Techniques are provided for generating a set of target hybrid seeds with optimal yield and risk performance, including a server receiving a candidate set of hybrid seeds along with probability of successful yield values, associated historical agricultural data and property information, and selecting a subset of the hybrid seeds that have probability of success values greater than a filtering threshold. The server generates representative yield values for hybrid seeds based on the historical agricultural data and risk values for each hybrid seed. The server generates a dataset of target hybrid seeds for planting based on the risk values, the yield values, and the properties for the target fields. The dataset of target hybrid seeds includes target hybrid seeds that meet a specific threshold for a range of risk values. The server causes display of the dataset of target hybrid seeds including yield values and risk values for the target fields.

Described herein are systems and methods for agricultural data analysis. In one embodiment, a computer system for monitoring field operations includes a database for storing agricultural data including yield and field data and at least one processing unit that is coupled to the database. The at least one processing unit is configured to execute instructions to monitor field operations, to store agricultural data, to automatically determine whether at least one correlation between different variables or parameters of the agricultural data exceeds a threshold, and to perform analysis of the agricultural data to identify a category of man-made issues or other issues that have potentially caused the correlation when at least one correlation occurs between different variables or parameters of the agricultural data.

Systems and methods for utilizing a spatial statistical model to maximize efficacy in performing trials on agronomic fields are disclosed herein. In an embodiment, a system receives first yield data for a first portion of an agronomic field, the first portion of the agronomic field having received a first treatment, and second yield data, for a second portion of the agronomic field, the second portion of the agronomic field having received a second treatment that is different than the first treatment. The system uses a spatial statistical model and the first yield data to compute a yield value for the second portion of the agronomic field, the yield value indicating an agronomic yield for the second portion of the agronomic field if the second portion of the agronomic field had received the first treatment instead of the second treatment. Based on the computed yield value and the second yield data, the system selects the second treatment. In an embodiment, in response to selecting the second treatment, the system generates a prescription map, the prescription map including the second treatment. The system may also generate one or more scripts which, when executed by an application controller, cause the application controller to control an operating parameter of an agricultural implement to apply the second treatment.

Systems and methods for scalable comparisons between two pixel maps are provided. In an embodiment, an agricultural intelligence computer system generates pixel maps from non-image data by transforming a plurality of values and location values into pixel values and pixel locations. The non-image data may include data relating to a particular agricultural field, such as nutrient content in the soil, pH values, soil moisture, elevation, temperature, and/or measured crop yields. The agricultural intelligence computer system converts each pixel map into a vector of values. The agricultural intelligence computer system also generates a matrix of metric coefficients where each value in the matrix of metric coefficients is computed using a spatial distance between to pixel locations in one of the pixel maps. Using the vectors of values and the matrix of metric coefficients, the agricultural intelligence computer system generates a difference metric identifying a difference between the two pixel maps. In an embodiment, the difference metric is normalized so that the difference metric is scalable to pixel maps of different sizes. The difference metric may then be used to select particular images that best match a measured yield, identify relationships between field values and measured crop yields, identify and/or select management zones, investigate management practices, and/or strengthen agronomic models of predicted yield.

Described herein are systems and method for monitoring weather conditions and controlling field operations based on the weather conditions. In one embodiment, a system for monitoring weather conditions for field operations includes a plurality of weather devices for monitoring weather conditions of fields with at least at one weather device in proximity to each field being monitored and at least one weather device having an electronics module for determining weather data including precipitation data. The system also includes an agricultural computer system having at least one processing unit for executing instructions for monitoring weather conditions. The at least one processing unit is configured to execute instructions to receive weather data from the plurality of weather devices for monitoring weather conditions of fields, to process the weather data, and to generate data including precipitation rate for monitoring weather conditions and operations of the plurality of weather devices.

Systems, methods, and devices are provided for monitoring precipitation. An example rain gauge device for use in such monitoring generally includes a first basin including at least one outlet for forming and releasing droplets of moisture, and at least two electrical contacts disposed proximate to the at least one outlet. A closed circuit is formed between the at least two electrical contacts when a droplet of moisture, released by the at least one outlet, contacts the at least two electrical contacts. The rain gauge device then also includes a processor communicatively coupled to the at least two electrical contacts. The processor is configured to determine presence of a moisture event based on the closed circuit formed by the droplet and the at least two electrical contacts and, in response to the determination, transmit an indication of the moisture event to a computing device.